The Complete Visual Guide to Machine Learning & Data Science udemy course free download

What you'll learn:

2023 Python for Machine Learning: A Step-by-Step Guide

  • The fundamental concepts and techniques of machine learning, including supervised and unsupervised learning
  • The implementation of various machine learning algorithms such as linear regression, logistic regression, k-nearest neighbors, decision trees, etc.
  • Techniques for building and evaluating machine learning models include feature selection, feature engineering, and model evaluation techniques.
  • The different types of model evaluation metrics, such as accuracy, precision, and recall, and how to interpret them.
  • Using machine learning libraries such as sci-kit-learn and pandas to build and evaluate models.
  • Hands-on experience working on real-world datasets and projects that will allow students to apply the concepts and techniques learned throughout.
  • The ability to analyze, interpret and present the results of machine learning models.
  • Understanding the trade-offs between different machine learning algorithms and their advantages and disadvantages.
  • Understanding best practices for developing, implementing, and interpreting machine learning models.
  • Skills in troubleshooting common machine learning problems and debugging machine learning models.

Requirements::

Description:

This course is for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science.

Build confidence with guided, step-by-step demos, and learn foundational skills from the ground up. Instead of memorizing complex math or learning a new coding language, we'll break down and explore machine learning techniques to help you understand exactly how and why they work.

Follow along with simple, visual examples and interact with user-friendly, Excel-based models to learn topics like linear and logistic regression, decision trees, KNN, naïve bayes, hierarchical clustering, sentiment analysis, and more – without writing a SINGLE LINE of code.

This course combines 4 best-selling courses from Maven Analytics into a single masterclass:

PART 1: Univariate & Multivariate Profiling

In Part 1 we’ll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We’ll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation:

Throughout the course, we’ll introduce real-world scenarios to solidify key concepts and simulate actual data science and business intelligence cases. You’ll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and more.

PART 2: Classification Modeling

In Part 2 we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting. From there we'll review common classification models like K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization:

You’ll help build a simple recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for an online travel company, extract sentiment from a sample of book reviews, and more.

PART 3: Regression & Forecasting

In Part 3 we’ll introduce core building blocks like linear relationships and least squared error, and practice applying them to univariate, multivariate, and non-linear regression models. We'll review diagnostic metrics like R-squared, mean error, F-significance, and P-Values, then use time-series forecasting techniques to identify seasonality, predict nonlinear trends, and measure the impact of key business decisions using intervention analysis:

You’ll see how regression analysis can be used to estimate property prices, forecast seasonal trends, predict sales for a new product launch, and even measure the business impact of a new website design.

PART 4: Unsupervised Learning

In Part 4 we’ll explore the differences between supervised and unsupervised machine learning and introduce several common unsupervised techniques, including cluster analysis, association mining, outlier detection and dimensionality reduction. We'll break down each model in simple terms and help you build an intuition for how they work, from K-means and apriori to outlier detection, principal component analysis, and more:

You'll see how K-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.

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Ready to dive in? Join today and get immediate, LIFETIME access to the following:

If you're an analyst or aspiring data professional looking to build the foundation for a successful career in machine learning or data science, you've come to the right place.

Happy learning!

-Josh & Chris

Who this course is for:

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